Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes.Aims
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To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability. This systematic review, following PRISMA guidelines and registered in PROSPERO, involved a comprehensive literature search using PubMed, Embase, Web of Science, and Scopus. Key search terms included “allograft”, “shoulder”, “humerus”, and “glenoid”. The review encompassed 37 studies with 456 patients, focusing on primary outcomes like failure rates and secondary outcomes such as PROMs and functional test results.Aims
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Preprint servers allow authors to publish full-text manuscripts or interim findings prior to undergoing peer review. Several preprint servers have extended their services to biological sciences, clinical research, and medicine. The purpose of this study was to systematically identify and analyze all articles related to Trauma & Orthopaedic (T&O) surgery published in five medical preprint servers, and to investigate the factors that influence the subsequent rate of publication in a peer-reviewed journal. All preprints covering T&O surgery were systematically searched in five medical preprint servers (medRxiv, OSF Preprints, Preprints.org, PeerJ, and Research Square) and subsequently identified after a minimum of 12 months by searching for the title, keywords, and corresponding author in Google Scholar, PubMed, Scopus, Embase, Cochrane, and the Web of Science. Subsequent publication of a work was defined as publication in a peer-reviewed indexed journal. The rate of publication and time to peer-reviewed publication were assessed. Differences in definitive publication rates of preprints according to geographical origin and level of evidence were analyzed.Aims
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Due to the overwhelming demand for trauma services, resulting from increasing emergency department attendances over the past decade, virtual fracture clinics (VFCs) have become the fashion to keep up with the demand and help comply with the BOA Standards for Trauma and Orthopaedics (BOAST) guidelines. In this article, we perform a systematic review asking, “How useful are VFCs?”, and what injuries and conditions can be treated safely and effectively, to help decrease patient face to face consultations. Our primary outcomes were patient satisfaction, clinical efficiency and cost analysis, and clinical outcomes. We performed a systematic literature search of all papers pertaining to VFCs, using the search engines PubMed, MEDLINE, and the Cochrane Database, according to the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) checklist. Searches were carried out and screened by two authors, with final study eligibility confirmed by the senior author.Background
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